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Datasets:
xzhong
/
ChemRAG

Tasks:
Question Answering
Languages:
English
Size:
10M<n<100M
ArXiv:
Tags:
medical
question answering
large language model
retrieval-augmented generation
Dataset card Data Studio Files Community
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+ ---
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+ task_categories:
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+ - question-answering
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+ language:
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+ - en
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+ tags:
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+ - medical
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+ - question answering
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+ - large language model
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+ - retrieval-augmented generation
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+ size_categories:
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+ - 10M<n<100M
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+ ---
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+ # The Wikipedia Corpus in MedRAG
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+
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+ This HF dataset contains the chunked snippets from the Wikipedia corpus used in [MedRAG](https://arxiv.org/abs/2402.13178). It can be used for medical Retrieval-Augmented Generation (RAG).
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+
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+ ## News
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+ - (02/26/2024) The "id" column has been reformatted. A new "wiki_id" column is added.
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+
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+ ## Dataset Details
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+
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+ ### Dataset Descriptions
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+
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+ As a large-scale open-source encyclopedia, Wikipedia is frequently used as a corpus in information retrieval tasks.
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+ We select Wikipedia as one of the corpora to see if the general domain database can be used to improve the ability of medical QA.
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+ We downloaded the processed Wikipedia data from [HuggingFace](https://huggingface.co/datasets/wikipedia) and chunked the text using [LangChain](https://www.langchain.com/) as snippets with no more than 1000 characters.
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+ This HF dataset contains our ready-to-use chunked snippets for the Wikipedia corpus, including 29,913,202 snippets with an average of 162 tokens.
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+
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+ ### Dataset Structure
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+ Each row is a snippet of Wikipedia, which includes the following features:
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+
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+ - id: a unique identifier of the snippet
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+ - title: the title of the Wikipedia article from which the snippet is collected
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+ - content: the content of the snippet
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+ - contents: a concatenation of 'title' and 'content', which will be used by the [BM25](https://github.com/castorini/pyserini) retriever
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the dataset is intended to be used. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section describes suitable use cases for the dataset. -->
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+
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+ ```shell
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+ git clone https://huggingface.co/datasets/MedRAG/wikipedia
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+ ```
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+
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+ ### Use in MedRAG
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+
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+ ```python
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+ >> from src.medrag import MedRAG
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+
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+ >> question = "A lesion causing compression of the facial nerve at the stylomastoid foramen will cause ipsilateral"
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+ >> options = {
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+ "A": "paralysis of the facial muscles.",
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+ "B": "paralysis of the facial muscles and loss of taste.",
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+ "C": "paralysis of the facial muscles, loss of taste and lacrimation.",
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+ "D": "paralysis of the facial muscles, loss of taste, lacrimation and decreased salivation."
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+ }
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+
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+ >> medrag = MedRAG(llm_name="OpenAI/gpt-3.5-turbo-16k", rag=True, retriever_name="MedCPT", corpus_name="Wikipedia")
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+ >> answer, snippets, scores = medrag.answer(question=question, options=options, k=32) # scores are given by the retrieval system
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+ ```
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+
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+ ## Citation
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+ ```shell
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+ @article{xiong2024benchmarking,
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+ title={Benchmarking Retrieval-Augmented Generation for Medicine},
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+ author={Guangzhi Xiong and Qiao Jin and Zhiyong Lu and Aidong Zhang},
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+ journal={arXiv preprint arXiv:2402.13178},
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+ year={2024}
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+ }
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+ ```
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